code: def test_create_train_X_y_output_when_y_is_series_exog_is_series_and_differentiation_is_1():
      """
      Test the output of create_train_X_y when using differentiation=1.
      """
      # Data differentiated
      diferenciator = TimeSeriesDifferentiator(order=1)
      data_diff = diferenciator.fit_transform(data)
      data_diff = pd.Series(data_diff, index=data.index).dropna()
  
      # Simulated exogenous variable
      rng = np.random.default_rng(9876)
      exog = pd.Series(
          rng.normal(loc=0, scale=1, size=len(data)),
          index=data.index,
          name="exog",
      )
      exog_diff = exog.iloc[1:]
      end_train = "2003-03-01 23:59:00"
  
      forecaster_1 = ForecasterAutoreg(LinearRegression(), lags=5)
      forecaster_2 = ForecasterAutoreg(
          LinearRegression(), lags=5, differentiation=1
      )
  
      X_train_1, y_train_1 = forecaster_1.create_train_X_y(
          y=data_diff.loc[:end_train], exog=exog_diff.loc[:end_train]
      )
      X_train_2, y_train_2 = forecaster_2.create_train_X_y(
          y=data.loc[:end_train], exog=exog.loc[:end_train]
      )
  
      pd.testing.assert_frame_equal(
          X_train_1, X_train_2, check_names=True
      )
      pd.testing.assert_series_equal(
          y_train_1, y_train_2, check_names=True
      )
  
imports: import re
  import pytest
  import numpy
  import pandas
  from skforecast.ForecasterAutoreg import ForecasterAutoreg
  from skforecast.preprocessing import TimeSeriesDifferentiator
  from skforecast.exceptions import MissingValuesExogWarning
  from sklearn.linear_model import LinearRegression
  from sklearn.compose import ColumnTransformer
  from sklearn.preprocessing import StandardScaler
  from sklearn.preprocessing import OneHotEncoder
  from .fixtures_ForecasterAutoreg import data
label_desc: Use image node_id 17 for calling a global function with example usage: test_create_train_X_y_output_when_y_is_series_exog_is_series_and_differentiation_is_1() without return types